- Time: TR 11:00 AM - 12:15 PM
- Location: 2080 Grainger Hall
- Instructor: Ilias Diakonikolas
- Instructor Email: email@example.com
- Instructor Office Hours: Friday 3-4.30pm
- Instructor Office Location: CS 4387 / Zoom
- TA: Nikos Zarifis
- TA Email: firstname.lastname@example.org
- TA Office Hours: Monday 3.30-5pm
- TA Office Location: CS4385
- Piazza Webpage (for discussion and notification):
- Homework Submission: Canvas
Note: for email, please put [CS760] in the subject title. Thanks!
This course is designed to give a graduate-level student a thorough grounding in the
methodologies, mathematics, and algorithms of machine learning. Topics covered include
supervised learning (neural networks, support vector machines, generative/discriminative learning),
unsupervised learning (clustering, GMM, PCA), and reinforcement learning. The course covers
theoretical concepts such as inductive bias, generalization, the PAC learning framework, etc.
Assignments include some written exercise and short programming
experiments with various learning algorithms.
Students entering the class are expected to have a background knowledge of probability, linear algebra, and calculus, and have good programming experience. The course will not provide a review on the background knowledge, or tutorials on programming.
These are all optional; accessing one or more of them is a good idea.
The instructors and TAs will post announcements, clarifications, hints, etc. on Piazza. You should
check the CS760 Piazza page frequently throughout the term. If you have a question, the best option
is to post a message on Piazza. The staff (instructors and TAs) will check the forum regularly, and if
you use the forum, other students will be able to help you too. When using the forum, please do not post
answers to homework questions before the homework is due.
The following rules are useful for asking questions on Piazza:
- Please check if someone has posted the same/similar question before you; it’s much easier if we build on the thread.
- Use an informative “Summary” line to help others.
If your question is personal or not of interest to other students, you may mark your question as private
on Piazza, so only the instructors will see it. If you wish to talk with one of us individually, you are
welcome to come to our office hours. Please reserve email for the questions you can't get answered in
office hours or through the forum.
The grading for the course will be be based on (tentative, subject to change):
- Homework Assignments (8 anticipated): 60%
- Midterm Exam: 20%
- Final Exam: 20%
General Homework Policies and Academic Misconduct
All homework assignments must be done individually.
Cheating and plagiarism will be dealt with in accordance with University procedures (see the Academic Misconduct Guide for Students).
For example, code for programming assignments must not be developed in groups, nor should code be shared.
You are encouraged to discuss with your peers, the TAs, or the instructor ideas, approaches and techniques broadly,
but not at a level of detail where specific implementation issues are described by anyone. If you have any questions on this,
please ask the instructor before you act.
Late Homework Policies
Homework assignments will include written problems and sometimes programming.
Accounts will be provided on the Computer Sciences Department's instructional
Unix workstations located in rooms 1350, 1351, and 1370. Homework is typically due the minute before class starts on the due date.
Late submissions will not be accepted.
Assignment grading questions must be
raised with the TAs within 72 hours after it is returned. Regrading request
for a part of a homework question may trigger the grader to regrade the entire
homework and could potentially take points off. Regrading will be done on the
original submitted work, no changes allowed.
We will drop the (single) lowest homework score from your final homework average calculation. The drop is
meant for emergency situations. We do not provide additional drops, late days, or homework extensions.
We encourage you to use a study group for doing your homework. Students are expected to help
each other out, and if desired, form ad-hoc homework groups.